Quantitative study of tectonic geomorphology along Haiyuan fault based on airborne LiDAR

Chinese Science Bulletin
By: , and 

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Abstract

High-precision and high-resolution topography are the fundamental data for active fault research. Light detection and ranging (LiDAR) presents a new approach to build detailed digital elevation models effectively. We take the Haiyuan fault in Gansu Province as an example of how LiDAR data may be used to improve the study of active faults and the risk assessment of related hazards. In the eastern segment of the Haiyuan fault, the Shaomayin site has been comprehensively investigated in previous research because of its exemplary tectonic topographic features. Based on unprecedented LiDAR data, the horizontal and vertical coseismic offsets at the Shaomayin site are described. The measured horizontal value is about 8.6 m, and the vertical value is about 0.8 m. Using prior dating ages sampled from the same location, we estimate the horizontal slip rate as 4.0 ± 1.0 mm/a with high confidence and define that the lower bound of the vertical slip rate is 0.4 ± 0.1 mm/a since the Holocene. LiDAR data can repeat the measurements of field work on quantifying offsets of tectonic landform features quite well. The offset landforms are visualized on an office computer workstation easily, and specialized software may be used to obtain displacement quantitatively. By combining precious chronological results, the fundamental link between fault activity and large earthquakes is better recognized, as well as the potential risk for future earthquake hazards.

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Publication type Article
Publication Subtype Journal Article
Title Quantitative study of tectonic geomorphology along Haiyuan fault based on airborne LiDAR
Series title Chinese Science Bulletin
DOI 10.1007/s11434-014-0199-4
Volume 59
Issue 20
Year Published 2014
Language English
Publisher Springer-Verlag
Contributing office(s) Earthquake Science Center
Description 14 p.
First page 2396
Last page 2409
Country China
State Gansu
Online Only (Y/N) N
Additional Online Files (Y/N) N
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